Reference & Startup

The documentation and examples within this tutorial were gleaned from the following resources:

tidycensus
tigris
tidyverse
Census Developers
Census Geography Program
Leaflet for R

As noted by its author, Kyle Walker, “tidycensus is an R package that allows users to interface with the US Census Bureau’s decennial Census and American Community Survey (ACS) APIs and return tidyverse-ready data frames, optionally with simple feature (sf) geometry included.”

To get started, load the tidycensus and tidyverse packages. Additional packages used within this RMarkdown file include tigris, readxl, writexl, arcgisbinding, leaflet, kableExtra, janitor, and extrafont.
A Census API key is also required, which can be obtained from http://api.census.gov/data/key_signup.html. Entry of the key using the census_api_key function only needs to occur once (i.e., it is tied to RStudio, rather than a single R script or Markdown file).

Install and load packages (uncomment as-needed).

# install.packages("tidyverse")
# install.packages("tidycensus")

# library(tidyverse)
# library(tidycensus)

# census_api_key("YOUR API KEY GOES HERE")

The following section contains examples of using the tidycensus package and Census API to retrieve, prepare, reshape, and blend demographic data for analysis and visualization. To save on processing time and avoid local memory limitations, data-intensive code chunks have been commented out (e.g., retrieving large amounts of census blocks using the tidycensus or tigris packages). Readers can view the underlying code while the input data is read-in as part of the R Project’s local data.

Review Variables

# Load the 2010 decennial census variables
DC2010_sf1 <- load_variables(2010, "sf1", cache = TRUE)
# View(DC2010_sf1)

# Load the 2014-2018 5-year ACS variables
ACS2018_acs5 <- load_variables(2018, "acs5", cache = TRUE)
# View(ACS2018_acs5)

# View a portion of the 2010 decennial census table
DC2010_sf1 %>% head() %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

name	label	concept
H001001	Total	HOUSING UNITS
H002001	Total	URBAN AND RURAL
H002002	Total!!Urban	URBAN AND RURAL
H002003	Total!!Urban!!Inside urbanized areas	URBAN AND RURAL
H002004	Total!!Urban!!Inside urban clusters	URBAN AND RURAL
H002005	Total!!Rural	URBAN AND RURAL

# View a portion of the 2018 ACS table
ACS2018_acs5 %>% head() %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

name	label	concept
B00001_001	Estimate!!Total	UNWEIGHTED SAMPLE COUNT OF THE POPULATION
B00002_001	Estimate!!Total	UNWEIGHTED SAMPLE HOUSING UNITS
B01001_001	Estimate!!Total	SEX BY AGE
B01001_002	Estimate!!Total!!Male	SEX BY AGE
B01001_003	Estimate!!Total!!Male!!Under 5 years	SEX BY AGE
B01001_004	Estimate!!Total!!Male!!5 to 9 years	SEX BY AGE

Usage

Example 1: Decennial Census

Retrieve the 2010 decennial census total population for all U.S. states.

Define a single, overarching location by default
Define a single variable manually

# Retrieve single variable and geographies for all U.S. states
st_2010_dc <- tidycensus::get_decennial( # API call
  geography = 'state', # Specify geography for which to retrieve data
  variables = 'P001001', # Specify variable
  year = 2010, # Specify year
  geometry = FALSE, # Include or omit spatial geometry
  output = 'wide', # Set to wide or tidy/long format
  cache_table = TRUE # Cache the table so it can be called quicker in the future, or not to save memory
)

# View data
st_2010_dc %>% head() %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

GEOID	NAME	P001001
01	Alabama	4779736
02	Alaska	710231
04	Arizona	6392017
05	Arkansas	2915918
06	California	37253956
22	Louisiana	4533372

Retrieve 2010 decennial census variables and geometries for a specific set of geographies

Define multiple locations manually
Define and name multiple variables manually
Retrieve geometries

# Retrieve variables and geographies for Multnomah, Washington, and Clackamas counties
co_2010_dc <- tidycensus::get_decennial( # API call
  geography = 'county', # Specify geography for which to retrieve data
  state = 'OR', # Specify state abbreviation
  county = c('Multnomah', 'Washington', 'Clackamas'), # Specify a list of counties
  variables = c(pop_tot = 'P001001', # Specify and name multiple variables
                pop_sex_m_tot = 'P012002',
                pop_sex_f_tot = 'P012026'),
  year = 2010, # Specify year
  geometry = TRUE, # Include or omit spatial geometry
  output = 'wide', # Set to wide or tidy/long format
  cache_table = TRUE # Cache the table so it can be called quicker in the future, or not to save memory
)

# View data
co_2010_dc %>% st_set_geometry(NULL) %>% head() %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

GEOID	NAME	pop_tot	pop_sex_m_tot	pop_sex_f_tot
41005	Clackamas County, Oregon	375992	184925	191067
41051	Multnomah County, Oregon	735334	363645	371689
41067	Washington County, Oregon	529710	260524	269186

Example 2: ACS

ACS data differ from decennial census data in that ACS data are based on an annual sample of households, rather than a complete enumeration. In turn, ACS data points are estimates characterized by a margin of error (MOE). tidycensus will always return the estimate and MOE for any requested variables. When requesting ACS data with tidycensus, it is not necessary to specify the “E” or “M” suffix for a variable name. Available survey types include ACS 5-year estimates (acs5) or ACS 1-year estimates (acs1). Note that the latter is only available for geographies with populations of 65,000 and greater.

Retrieve 2018 ACS 5-year variables and geometries for a specific set of geographies.

Define multiple locations manually
Define and name multiple variables manually
Retrieve geometries, specifically the TIGER/Line shapefiles

Concerning geometries, tidycensus used the geographic coordinate system NAD 1983 (EPSG: 4269), which is the default for Census spatial data files. tidycensus uses the Census cartographic boundary shapefiles for faster processing; if you prefer the TIGER/Line shapefiles (i.e., Topologically Integrated Geographic Encoding and Referencing), set cb = FALSE in the function call. Per Census documentation, the cartographic boundary files are simplified representations of selected geographic areas from the Census Bureau’s Master Address File (MAF)/TIGER geographic database. These boundary files are specifically designed for small scale thematic mapping. When possible, generalization is performed with intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. To improve the appearance of shapes, areas are represented with fewer vertices than detailed TIGER/Line equivalents. Some small holes or discontiguous parts of areas are not included in generalized files. Generalized boundary files are clipped to a simplified version of the U.S. outline. As a result, some off-shore areas may be excluded from the generalized files. Consult this TIGER Data Products Guide to determine which file type is best for your purposes.

# Retrieve variables and geographies for Multnomah, Washington, and Clackamas counties
co_2018_acs5 <- tidycensus::get_acs(
  geography = 'county', # Specify geography for which to retrieve data
  state = 'OR', # Specify state abbreviation
  county = c('Multnomah', 'Washington', 'Clackamas'), # Specify a list of counties
  variables = c(hh_medinc_total = 'B19013_001', # Specify and name multiple variables
                hh_foodst_total = 'B22003_001', 
                hh_foodst_rec = 'B22003_002'),
  year = 2018, # Specify year
  survey = 'acs5', # Specify survey type
  geometry = TRUE, # Include or omit spatial geometry
  cb = FALSE, # Specify cartographic boundary files or TIGER/Line shapefiles
  output = 'wide', # Set to wide or tidy/long format
  cache_table = TRUE # Cache the table so it can be called quicker in the future, or not to save memory
)

# View data
co_2018_acs5 %>% st_set_geometry(NULL) %>% head() %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

GEOID	NAME	hh_medinc_totalE	hh_medinc_totalM	hh_foodst_totalE	hh_foodst_totalM	hh_foodst_recE	hh_foodst_recM
41005	Clackamas County, Oregon	76597	1050	155456	776	16694	859
41067	Washington County, Oregon	78010	1086	216507	1013	22213	1020
41051	Multnomah County, Oregon	64337	779	321968	1216	55018	1390

Concerning the structure of the data frame, a wide format contains a single row for each observation with many columns representing all variables (human-readable), while a long/tidy format contains many rows per observation (assuming more than one variable) with name-value pairs for each variable and associated value (machine-readable. For more details, see Hadley Wickham’s seminal paper Tidy Data.

Retrieve 2015 ACS 1-year variables and geometries for a specific set of geographies.

Define multiple locations manually
Define and name multiple variables manually
Retrieve geometries, specifically the cartographic boundaries
Output in long/tidy format

# Retrieve variables and geographies for Multnomah, Washington, and Clackamas counties
co_2015_acs1 <- tidycensus::get_acs(
  geography = 'county', # Specify geography for which to retrieve data
  state = 'OR', # Specify Oregon
  county = c('Multnomah', 'Washington', 'Clackamas'), # Specify a list of counties
  variables = c(hh_medinc_total = 'B19013_001', # Specify and name multiple variables
                hh_foodst_total = 'B22003_001', 
                hh_foodst_rec = 'B22003_002'),
  year = 2015, # Specify year
  survey = 'acs1', # Specify survey type
  geometry = T, # Include or omit spatial geometry
  # By default, cartographic boundary files are used
  output = 'tidy', # Set to wide or tidy/long format
  cache_table = T # Cache the table so it can be called quicker in the future, or not to save memory
)

Compare the structure of the data sets.

# View the 2018 ACS 5-year table in wide format
co_2018_acs5 %>% st_set_geometry(NULL) %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

GEOID	NAME	hh_medinc_totalE	hh_medinc_totalM	hh_foodst_totalE	hh_foodst_totalM	hh_foodst_recE	hh_foodst_recM
41005	Clackamas County, Oregon	76597	1050	155456	776	16694	859
41067	Washington County, Oregon	78010	1086	216507	1013	22213	1020
41051	Multnomah County, Oregon	64337	779	321968	1216	55018	1390

# View the 2015 ACS 1-year table in long/tidy format
co_2015_acs1 %>% st_set_geometry(NULL) %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

GEOID	NAME	variable	estimate	moe
41005	Clackamas County, Oregon	hh_medinc_total	69629	2898
41005	Clackamas County, Oregon	hh_foodst_total	152414	1886
41005	Clackamas County, Oregon	hh_foodst_rec	17527	1737
41051	Multnomah County, Oregon	hh_medinc_total	59231	2100
41051	Multnomah County, Oregon	hh_foodst_total	311797	3172
41051	Multnomah County, Oregon	hh_foodst_rec	61844	2753
41067	Washington County, Oregon	hh_medinc_total	70447	1703
41067	Washington County, Oregon	hh_foodst_total	211139	2446
41067	Washington County, Oregon	hh_foodst_rec	24253	2324

Retrieve 2018 ACS 5-year variables and geometries for all geographies within a larger geography (e.g., all counties within a state).

Define multiple locations with a vector
Define and name multiple variables manually
Retrieve geometries, specifically the cartographic boundaries

# Create a vector of county FIPS codes for use in the `tidycensus` call
county_vector <- tidycensus::fips_codes %>% # Retrieve table of all state and county names and FIPS codes
  filter(state_name == "Oregon") %>% # Query Oregon counties
  select(county_code, county) # Maintain only the county code and name variables

# Retrieve variables and geometries for counties within the vector
co_OR_2018_acs5 <- tidycensus::get_acs(
  geography = 'county', # Specify geography for which to retrieve data
  state = 'OR', # Specify state abbreviation
  county = county_vector$county_code, # Vector of all counties
  variables = c(hh_medinc_total = 'B19013_001', # Specify and name multiple variables
                hh_foodst_total = 'B22003_001', 
                hh_foodst_rec = 'B22003_002'),
  year = 2018, # Specify year
  survey = 'acs5', # Specify survey type
  geometry = TRUE, # Include or omit spatial geometry
  cb = TRUE, # Specify cartographic boundary files or TIGER/Line shapefiles
  output = 'tidy', # Set to wide or tidy/long format
  cache_table = TRUE # Cache the table so it can be called quicker in the future, or not to save memory
)

# View data
co_OR_2018_acs5 %>% st_set_geometry(NULL) %>% head() %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

GEOID	NAME	variable	estimate	moe
41001	Baker County, Oregon	hh_medinc_total	43921	2509
41001	Baker County, Oregon	hh_foodst_total	6927	271
41001	Baker County, Oregon	hh_foodst_rec	1280	168
41003	Benton County, Oregon	hh_medinc_total	58655	1968
41003	Benton County, Oregon	hh_foodst_total	35056	451
41003	Benton County, Oregon	hh_foodst_rec	4082	486

Example 3: Tigris

In some cases, you may want use the tabular and spatial data separately or may want to join the two data sets after analysis. In those cases, tidycensus can be used in combination with the tigris package.

Retrieve tract data for all counties within Oregon.

Use a vector of variables
Use a vector of locations
Use tidyensus to retrieve tabular data
Use tigris to retrieve spatial data
Join the tabular and spatial data

# Create a vector of variables for use in the `tidycensus` call.
DC2010_sf1_var <- c(pop_sex_total = 'P012001',
                pop_sex_m_tot = 'P012002',
                pop_sex_m_00_04 = 'P012003',
                pop_sex_m_05_09 = 'P012004',
                pop_sex_m_10_14 = 'P012005',
                pop_sex_m_15_17 = 'P012006',
                pop_sex_m_18_19 = 'P012007',
                pop_sex_f_tot = 'P012026',
                pop_sex_f_00_04 = 'P012027',
                pop_sex_f_05_09 = 'P012028', 
                pop_sex_f_10_14 = 'P012029', 
                pop_sex_f_15_17 = 'P012030', 
                pop_sex_f_18_19 = 'P012031')

# Retrieve census block variables for all Oregon counties
tr_2010_dc <- tidycensus::get_decennial(
geography = 'tract', # Specify geography for which to retrieve data
variables = DC2010_sf1_var, # Vector of variables
state = 'OR', # Specify state abbreviation
county = county_vector$county_code, # Vector of all counties
year = 2010, # Specify year
geometry = F, # Include or omit spatial geometry
output = 'wide', # Set to wide or long/tidy format
cache_table = T # Cache the table so it can be called quicker in the future
)

Retrieve shapes for all Oregon tracts via the tigris package.

# By default `tigris` retrieves the most recent vintage of a dataset; specify year as-needed
# The default coordinate reference system (CRS) is NAD83 (EPSG 4269 https://spatialreference.org/ref/epsg/nad83/)
# If cb is set to TRUE, download a generalized (1:500k) tracts file. Defaults to (the most detailed TIGER/Line file)

gc() # A call of gc causes a garbage collection to take place. The primary purpose of calling gc is for the report on memory usage. Use this before and after a data-heavy processing task.
tr_2010_dc_shp <- tigris::tracts("OR", year = 2010, cb = FALSE)
gc()

Join the attributes to the geometries.

tr_2010_dc_geo <- tr_2010_dc_shp %>%
  inner_join(tr_2010_dc, by = c("GEOID10" = "GEOID"))

class(tr_2010_dc_geo)
st_crs(tr_2010_dc_geo)

Note that the resulting object’s class is dependent on the join order. The left side’s class takes priority; therefore, in the above, the attributes (right side) are being joined to the geometries (left side), so the resulting object class is sf. If the order is flipped (below) and the geometries (right side) are joined to the attributes (let side), the object class is not sf. To make it so, add %>% st_as_sf()

tr_2010_dc_geo_REVERSE1 <- tr_2010_dc %>%
  inner_join(tr_2010_dc_shp, by = c("GEOID" = "GEOID10"))

class(tr_2010_dc_geo_REVERSE1)
st_crs(tr_2010_dc_geo_REVERSE1)

tr_2010_dc_geo_REVERSE2 <- tr_2010_dc %>%
  inner_join(tr_2010_dc_shp, by = c("GEOID" = "GEOID10")) %>%
  st_as_sf()

class(tr_2010_dc_geo_REVERSE2)
st_crs(tr_2010_dc_geo_REVERSE2)

Example 4: Aggregation

[At a time in 2019] Retrieving all decennial census block group data for a specified state or county generates an error. This has since been resolved, but it provides a good example of how smaller nested geographies can be aggregated to larger geographies (e.g., block to block groups).

This works now…

bg_2010_dc <- tidycensus::get_decennial( # API call
  geography = 'block group', # Specify geography for which to retrieve data
  state = 'WA', # Specify state abbreviation
  county = 'King', # Specify county
  variables = 'P001001', # Specify variable
  year = 2010, # Specify year
  geometry = FALSE, # Include or omit spatial geometry
  output = 'wide', # Set to wide or tidy/long format
  cache_table = TRUE # Cache the table so it can be called quicker in the future, or not to save memory
)

But if it didn’t…

Retrieve block data, create the block group GEOID by removing the last 3 characters in the block GEOID, and group by/summarize to block group.

# Retrieve decennial census variables for all blocks
bl_2010_dc <- tidycensus::get_decennial(
  geography = 'block', # Specify statistical area for which to retrieve data
  state = 'WA', # Specify state abbreviation
  county = 'King', # Specify county
  variables = 'P001001',
  year = 2010, # Specify year
  geometry = FALSE, # Include or omit spatial geometry
  output = 'wide', # Set to wide or tidy/long format
  cache_table = TRUE # Cache the table so it can be called quicker in the future, or not to save memory
)

Aggregate data to block groups.

bl_2010_dc_to_bg <- bl_2010_dc %>% 
  mutate(GEOID_BG = stringr::str_sub(GEOID, 1, -4)) %>% # Create BG GEOID
  select(GEOID_BG, 1:length(.), -GEOID, -NAME) %>% # Reorder/remove columns
  rename(GEOID = GEOID_BG) %>% # Rename GEOID
  gather(variable, est, 2:length(.)) %>% # Reshape from wide to long form by creating name-value pairs for all variables
  group_by(GEOID, variable) %>% # Group by GEOID and variable names
  summarize(est = sum(est, na.rm = T)) %>% # Sum values per group
  ungroup() %>% # Ungroup
  spread(variable, est) # Reshape from long to wide form by extending values into their own columns

Compare the data sets.

bgbl_2010_dc <- bg_2010_dc %>%
  inner_join(bl_2010_dc_to_bg, by = 'GEOID')

# View the comparison data set
bgbl_2010_dc %>% head() %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

GEOID	NAME	P001001.x	P001001.y
530330001001	Block Group 1, Census Tract 1, King County, Washington	1250	1250
530330001002	Block Group 2, Census Tract 1, King County, Washington	1234	1234
530330001003	Block Group 3, Census Tract 1, King County, Washington	1337	1337
530330001004	Block Group 4, Census Tract 1, King County, Washington	1492	1492
530330001005	Block Group 5, Census Tract 1, King County, Washington	942	942
530330002001	Block Group 1, Census Tract 2, King County, Washington	1086	1086

Visualization

ggplot2 is a data visualization package that is part of the tidyverse.

Create a plot of 2010 decennial census state populations.

st_2010_dc %>%
  ggplot(aes(x = P001001, y = reorder(NAME, P001001,))) + # Set the x-axis to the variable and the y-axis to the observation (sort in descending order)
  ggtitle("2010 State Population") + # Add a title
  xlab("State Name") + # Name the x-axis
  ylab("Total Population") + # Name the y-axis
  geom_point() # Add point geometry

Create a plot of 2014-2018 ACS 5-year estimates and MOEs for all Oregon counties.

co_OR_2018_acs5 %>%
  mutate(NAME = gsub(" County, Oregon", "", NAME)) %>%
  filter(variable == 'hh_medinc_total') %>%
  ggplot(aes(x = estimate, y = reorder(NAME, estimate))) +
  geom_errorbarh(aes(xmin = estimate - moe, xmax = estimate + moe)) +
  geom_point(color = "red", size = 3) +
  labs(title = "Median Household Income by County",
       subtitle = "2014-2018 (5-year) American Community Survey",
       y = "",
       x = "ACS estimate (bars represent margin of error)")

Create a choropleth map of a single variable across geographies within a single county.

# Retrieve variables and geographies for census block groups in Multnomah County, Oregon
bg_Mult_2018_acs5 <- tidycensus::get_acs( # API call
  geography = 'block group', # Specify statistical area for which to retrieve data
  state = 'OR', # Specify state name
  county = c('Multnomah'), # Specify county name
  variables = c(hh_medinc_total = 'B19013_001'),
  year = 2018, # Specify year
  survey = 'acs5', # Specify survey type
  geometry = T, # Include or omit spatial geometry
  output = 'tidy', # Set to wide or tidy/long format
  cache_table = T # Cache the table so it can be called quicker in the future, or not to save memory
)

# Create choropleth map
bg_Mult_2018_acs5 %>%
  ggplot(aes(fill = estimate)) + # Specify the aesthetic as fill based on the estimate values
  geom_sf(color = NA) + # Specify outline color
  coord_sf(crs = 26910) + # Specify coordinate system/projection (e.g., UTM10N)
  ggtitle("Multnomah County Median Household Income", subtitle = "2014-2018 (5-year) ACS Data") + # Specify title and subtitle
  scale_fill_viridis_c(option = "magma") # Specify fill symbology

Created faceted choropleths maps of multiple variables across geographies within a single county.

As mentioned by Kyle Walker in his tidycensus tutorial, “one of the most powerful features of ggplot2 is its support for small multiples, which works very well with the tidy data format returned by tidycensus. Many Census and ACS variables return counts, which are generally inappropriate for choropleth mapping. In turn, get_decennial and get_acs have an optional argument, summary_var, that can work as a multi-group denominator when appropriate.” For example, view the racial/ethnic population distribution within a given county.

# Create a vector of variables 
DC2010_sf1_race_vars <- c(White = "P005003", 
              Black = "P005004", 
              Asian = "P005006", 
              Hispanic = "P004003")

# Retrieve variables and geographies for census block groups in Multnomah County, Oregon
bg_Mult_2010_dc <- tidycensus::get_decennial( # API call
  geography = 'tract', # Specify statistical area for which to retrieve data
  state = 'OR', # Specify state name
  county = c('Multnomah'), # Specify county name
  variables = DC2010_sf1_race_vars,
  year = 2010, # Specify year
  geometry = T, # Include or omit spatial geometry
  output = 'tidy', # Set to wide or tidy/long format
  summary_var = "P001001" # Specify summary variable (e.g., total population)
)

bg_Mult_2010_dc %>%
  mutate(pct = 100 * (value / summary_value)) %>% # Calculate the race/ethnic proportion of the total pop. 
  ggplot(aes(fill = pct)) + # Specify the aesthetic as fill based on the proportion values
  facet_wrap(~variable) + # Facet wrap on the race/ethnicity
  geom_sf(color = NA) + # Specify outline color
  coord_sf(crs = 26910) + # Specify coordinate system/projection (e.g., UTM10N)
  ggtitle("Multnomah County", subtitle = "Population by Race/Ethnicity") + # Specify title and subtitle
  scale_fill_viridis_c() # Specify fill symbology

Create an unformatted, interactive map using mapview.

co_OR_2018_acs5 %>%
  mapview(.)

Create a formatted, interactive map using leaflet.

# Use leaflet's color ramp creator:
medinc_colors <- leaflet::colorQuantile(
  palette = 'Greens', # This will accept any ColorBrewer color pallette
  domain = bg_Mult_2018_acs5$estimate, # Specify the variable
  n = 9 # Specify the number of breaks
)

leaflet() %>% 
  addTiles('http://{s}.tile.stamen.com/toner/{z}/{x}/{y}.png') %>%
  addPolygons(
    data = filter(co_2010_dc, NAME == "Multnomah County, Oregon"),
    weight = 3,
    fillOpacity = 0,
    color = 'royalblue',
    group = "Counties (2010 DC)",
    popup = paste0(
      "<b>2010 County</b> ",
      paste0("<br><b>Name</b>: ", co_2010_dc$NAME, "<br><b>Total Population</b>: ", prettyNum(co_2010_dc$pop_tot, ','))
    )
  ) %>%
  addPolygons(
    data = bg_Mult_2018_acs5, 
    weight = 1,
    color = 'black',
    fillColor = medinc_colors(bg_Mult_2018_acs5$estimate),
    fillOpacity = 0.7,
    group = "Block Groups (2018 ACS 5-year)",
    popup = paste0(
      "<b>2018 ACS 5-year Block Group</b> ",
      paste0("<br><b>Median HH Income Estimate</b>: ", prettyNum(bg_Mult_2018_acs5$estimate, ','), "<br><b>Median HH Income MOE</b>: ", prettyNum(bg_Mult_2018_acs5$moe, ','))
    )
  ) %>%
  # Layers control
  addLayersControl(
    overlayGroups = c("Counties (2010 DC)", "Block Group (2018 ACS 5-year)"),
    options = layersControlOptions(collapsed = FALSE)
  )

Export and Import

Save out data frames to various formats.

# Export files
# write_xlsx(list('co_OR_2018_acs5' = co_OR_2018_acs5), "./Data/co_OR_2018_acs5.xlsx")
# st_write(co_OR_2018_acs5,"./Data/co_OR_2018_acs5.shp")
# saveRDS(co_OR_2018_acs5,"./Data/co_OR_2018_acs5.rds")
# arc.write(path = "./Data/R.gdb/co_OR_2018_acs5", data = co_OR_2018_acs5, shape_info = list(type = 'Polygon', WKT = 'GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]'), overwrite = T)

Read in data to avoid duplicative calls to Census API.

# Read in RDS dataset already retrieved via `tidycensus`
# co_OR_2018_acs5_XLSX  <- read_excel("./Data/co_OR_2018_acs5.xlsx", sheet = "co_OR_2018_acs5")
# co_OR_2018_acs5_SHP  <- st_read("./Data/co_OR_2018_acs5.shp")
# co_OR_2018_acs5_RDS <- readRDS("./Data/co_OR_2018_acs5.rds")

A work by Alex Brasch

tidycensus Tutorial

Alex Brasch

4/15/2020